【基于ALS模型的教育视频推荐系统(Java实现)】
【基于ALS模型的教育视频推荐系统(Java实现)】
下面是一个完整的基于交替最小二乘法(ALS)的教育视频推荐系统实现，包含数据预处理、模型训练、推荐生成和评估模块。
1. 系统架构
edu-recommender/
├── src/
│ ├── main/
│ │ ├── java/
│ │ │ ├── model/ # 数据模型
│ │ │ ├── algorithm/ # ALS算法实现
│ │ │ ├── service/ # 业务逻辑
│ │ │ ├── util/ # 工具类
│ │ │ └── Main.java # 入口类
│ │ └── resources/ # 配置文件
├── pom.xml # Maven依赖
└── data/ # 示例数据集
2.1 数据模型类
// Video.java
package model;public class Video {private int id;private String title;private String category;private double duration; // 分钟// 构造函数、getter和setter
}// User.java
package model;public class User {private int id;private String username;private String educationLevel;// 构造函数、getter和setter
}// Rating.java
package model;public class Rating {private int userId;private int videoId;private double score; // 1-5分// 构造函数、getter和setter
}
2.2 ALS算法实现
package algorithm;import java.util.*;public class ALS {private int numFeatures; // 特征维度private double lambda; // 正则化参数private int maxIter; // 最大迭代次数private double[][] userFeatures; // 用户特征矩阵private double[][] itemFeatures; // 物品特征矩阵public ALS(int numFeatures, double lambda, int maxIter) {this.numFeatures = numFeatures;this.lambda = lambda;this.maxIter = maxIter;}// 训练模型public void train(List<Rating> ratings, int numUsers, int numItems) {// 初始化特征矩阵Random rand = new Random();userFeatures = new double[numUsers][numFeatures];itemFeatures = new double[numItems][numFeatures];for (int i = 0; i < numUsers; i++) {for (int j = 0; j < numFeatures; j++) {userFeatures[i][j] = rand.nextDouble();}}for (int i = 0; i < numItems; i++) {for (int j = 0; j < numFeatures; j++) {itemFeatures[i][j] = rand.nextDouble();}}// 交替优化for (int iter = 0; iter < maxIter; iter++) {// 固定物品特征，优化用户特征updateFeatures(ratings, userFeatures, itemFeatures, true);// 固定用户特征，优化物品特征updateFeatures(ratings, itemFeatures, userFeatures, false);double error = calculateRMSE(ratings);System.out.printf("Iteration %d, RMSE: %.4f\n", iter, error);}}// 更新特征矩阵private void updateFeatures(List<Rating> ratings, double[][] mainFeatures, double[][] fixedFeatures, boolean isUser) {Map<Integer, List<Rating>> groupedRatings = groupRatings(ratings, isUser);for (Map.Entry<Integer, List<Rating>> entry : groupedRatings.entrySet()) {int id = entry.getKey();List<Rating> group = entry.getValue();// 构建矩阵A和向量bdouble[][] A = new double[numFeatures][numFeatures];double[] b = new double[numFeatures];for (Rating r : group) {int otherId = isUser ? r.getVideoId() : r.getUserId();double[] otherVec = fixedFeatures[otherId];// A += otherVec * otherVec^Tfor (int i = 0; i < numFeatures; i++) {for (int j = 0; j < numFeatures; j++) {A[i][j] += otherVec[i] * otherVec[j];}b[i] += otherVec[i] * r.getScore();}}// 添加正则化项: A += lambda * Ifor (int i = 0; i < numFeatures; i++) {A[i][i] += lambda;}// 解线性方程组: A * x = bdouble[] newFeatures = solveLinearSystem(A, b);System.arraycopy(newFeatures, 0, mainFeatures[id], 0, numFeatures);}}// 分组评分数据private Map<Integer, List<Rating>> groupRatings(List<Rating> ratings, boolean byUser) {Map<Integer, List<Rating>> map = new HashMap<>();for (Rating r : ratings) {int key = byUser ? r.getUserId() : r.getVideoId();map.computeIfAbsent(key, k -> new ArrayList<>()).add(r);}return map;}// 解线性方程组(使用高斯消元法)private double[] solveLinearSystem(double[][] A, double[] b) {int n = b.length;double[] x = new double[n];// 前向消元for (int i = 0; i < n; i++) {// 找主元int maxRow = i;for (int k = i + 1; k < n; k++) {if (Math.abs(A[k][i]) > Math.abs(A[maxRow][i])) {maxRow = k;}}// 交换行double[] tempRow = A[i];A[i] = A[maxRow];A[maxRow] = tempRow;double tempVal = b[i];b[i] = b[maxRow];b[maxRow] = tempVal;// 消元for (int k = i + 1; k < n; k++) {double factor = A[k][i] / A[i][i];b[k] -= factor * b[i];for (int j = i; j < n; j++) {A[k][j] -= factor * A[i][j];}}}// 回代for (int i = n - 1; i >= 0; i--) {double sum = 0;for (int j = i + 1; j < n; j++) {sum += A[i][j] * x[j];}x[i] = (b[i] - sum) / A[i][i];}return x;}// 计算RMSEpublic double calculateRMSE(List<Rating> ratings) {double sumSquaredError = 0;for (Rating r : ratings) {double predicted = predict(r.getUserId(), r.getVideoId());sumSquaredError += Math.pow(predicted - r.getScore(), 2);}return Math.sqrt(sumSquaredError / ratings.size());}// 预测评分public double predict(int userId, int videoId) {double score = 0;for (int i = 0; i < numFeatures; i++) {score += userFeatures[userId][i] * itemFeatures[videoId][i];}return Math.max(1, Math.min(5, score)); // 限制在1-5分}// 为用户推荐视频public List<Integer> recommendVideos(int userId, int numRecommendations, int numVideos) {PriorityQueue<VideoScore> pq = new PriorityQueue<>();for (int videoId = 0; videoId < numVideos; videoId++) {double score = predict(userId, videoId);pq.offer(new VideoScore(videoId, score));if (pq.size() > numRecommendations) {pq.poll();}}List<Integer> recommendations = new ArrayList<>();while (!pq.isEmpty()) {recommendations.add(0, pq.poll().videoId);}return recommendations;}// 辅助类private static class VideoScore implements Comparable<VideoScore> {int videoId;double score;VideoScore(int videoId, double score) {this.videoId = videoId;this.score = score;}@Overridepublic int compareTo(VideoScore other) {return Double.compare(this.score, other.score);}}
}
2.3 推荐服务类
package service;import model.*;
import algorithm.ALS;
import java.util.*;public class RecommendationService {private List<User> users;private List<Video> videos;private List<Rating> ratings;private ALS alsModel;public RecommendationService(List<User> users, List<Video> videos, List<Rating> ratings) {this.users = users;this.videos = videos;this.ratings = ratings;}// 训练推荐模型public void trainModel() {int numUsers = users.size();int numVideos = videos.size();alsModel = new ALS(10, 0.01, 20); // 10个特征，lambda=0.01，20次迭代alsModel.train(ratings, numUsers, numVideos);}// 为用户生成推荐public List<Video> getRecommendations(int userId, int numRecs) {List<Integer> videoIds = alsModel.recommendVideos(userId, numRecs, videos.size());List<Video> recommendations = new ArrayList<>();for (int videoId : videoIds) {recommendations.add(videos.get(videoId));}return recommendations;}// 评估推荐系统public void evaluate() {// 划分训练集和测试集Collections.shuffle(ratings);int split = (int) (ratings.size() * 0.8);List<Rating> trainSet = ratings.subList(0, split);List<Rating> testSet = ratings.subList(split, ratings.size());// 在训练集上训练ALS tempModel = new ALS(10, 0.01, 20);tempModel.train(trainSet, users.size(), videos.size());// 在测试集上评估double rmse = tempModel.calculateRMSE(testSet);System.out.printf("Test RMSE: %.4f\n", rmse);}
}
2.4 主程序
import model.*;
import service.RecommendationService;
import java.util.*;public class Main {public static void main(String[] args) {// 1. 准备示例数据List<User> users = createUsers();List<Video> videos = createVideos();List<Rating> ratings = createRatings(users, videos);// 2. 创建推荐服务RecommendationService service = new RecommendationService(users, videos, ratings);// 3. 训练模型service.trainModel();// 4. 生成推荐int targetUserId = 0; // 为第一个用户推荐List<Video> recommendations = service.getRecommendations(targetUserId, 5);System.out.println("为用户 " + users.get(targetUserId).getUsername() + " 推荐的视频:");for (Video video : recommendations) {System.out.println("- " + video.getTitle() + " (" + video.getCategory() + ")");}// 5. 评估模型service.evaluate();}private static List<User> createUsers() {List<User> users = new ArrayList<>();users.add(new User(0, "张三", "本科"));users.add(new User(1, "李四", "硕士"));users.add(new User(2, "王五", "博士"));return users;}private static List<Video> createVideos() {List<Video> videos = new ArrayList<>();videos.add(new Video(0, "Java入门", "编程", 120));videos.add(new Video(1, "机器学习基础", "AI", 180));videos.add(new Video(2, "高等数学", "数学", 240));videos.add(new Video(3, "英语写作", "语言", 90));videos.add(new Video(4, "数据结构", "编程", 150));videos.add(new Video(5, "深度学习", "AI", 210));videos.add(new Video(6, "线性代数", "数学", 160));videos.add(new Video(7, "商务英语", "语言", 95));return videos;}private static List<Rating> createRatings(List<User> users, List<Video> videos) {List<Rating> ratings = new ArrayList<>();Random rand = new Random();// 为每个用户随机评分一些视频for (User user : users) {int numRatings = 3 + rand.nextInt(3); // 每个用户3-5个评分Set<Integer> ratedVideos = new HashSet<>();for (int i = 0; i < numRatings; i++) {int videoId;do {videoId = rand.nextInt(videos.size());} while (ratedVideos.contains(videoId));ratedVideos.add(videoId);double score = 1 + rand.nextInt(5); // 1-5分ratings.add(new Rating(user.getId(), videoId, score));}}return ratings;}
}
3. 系统优化建议
冷启动问题解决方案：
// 在RecommendationService中添加混合推荐方法
public List<Video> getHybridRecommendations(int userId, int numRecs) {// 如果新用户，使用基于内容的推荐if (isNewUser(userId)) {return getContentBasedRecommendations(userId, numRecs);}// 否则使用ALS推荐return getRecommendations(userId, numRecs);
}private boolean isNewUser(int userId) {return ratings.stream().noneMatch(r -> r.getUserId() == userId);
}private List<Video> getContentBasedRecommendations(int userId, int numRecs) {User user = users.get(userId);// 根据用户教育水平推荐同类视频return videos.stream().filter(v -> v.getCategory().equals(getPreferredCategory(user))).sorted(Comparator.comparingDouble(Video::getDuration).reversed()).limit(numRecs).collect(Collectors.toList());
}private String getPreferredCategory(User user) {// 简单逻辑：根据教育水平推荐类别switch(user.getEducationLevel()) {case "本科": return "编程";case "硕士": return "AI";case "博士": return "数学";default: return "语言";}
}
实时更新模型：
// 在ALS类中添加增量更新方法
public void updateModel(Rating newRating) {int userId = newRating.getUserId();int videoId = newRating.getVideoId();// 简单实现：重新计算相关用户和视频的特征updateUserFeatures(userId);updateVideoFeatures(videoId);
}private void updateUserFeatures(int userId) {// 获取该用户的所有评分List<Rating> userRatings = ratings.stream().filter(r -> r.getUserId() == userId).collect(Collectors.toList());// 重新计算用户特征(简化版)double[] newFeatures = new double[numFeatures];for (Rating r : userRatings) {for (int i = 0; i < numFeatures; i++) {newFeatures[i] += itemFeatures[r.getVideoId()][i] * r.getScore();}}userFeatures[userId] = newFeatures;
}
性能优化：
// 使用矩阵运算库替代手动实现
import org.apache.commons.math3.linear.*;// 修改ALS中的solveLinearSystem方法
private double[] solveLinearSystem(double[][] A, double[] b) {RealMatrix matrix = MatrixUtils.createRealMatrix(A);DecompositionSolver solver = new LUDecomposition(matrix).getSolver();return solver.solve(MatrixUtils.createRealVector(b)).toArray();
}
- 评估指标扩展
// 在RecommendationService中添加更多评估指标
public void fullEvaluation() {// 1. 划分训练测试集Collections.shuffle(ratings);int split = (int) (ratings.size() * 0.8);List<Rating> trainSet = ratings.subList(0, split);List<Rating> testSet = ratings.subList(split, ratings.size());// 2. 训练模型ALS tempModel = new ALS(10, 0.01, 20);tempModel.train(trainSet, users.size(), videos.size());// 3. 计算各项指标double rmse = calculateRMSE(tempModel, testSet);double precision = calculatePrecision(tempModel, testSet);double recall = calculateRecall(tempModel, testSet);System.out.println("=== 评估结果 ===");System.out.printf("RMSE: %.4f\n", rmse);System.out.printf("Precision@5: %.4f\n", precision);System.out.printf("Recall@5: %.4f\n", recall);
}private double calculateRMSE(ALS model, List<Rating> testRatings) {return model.calculateRMSE(testRatings);
}private double calculatePrecision(ALS model, List<Rating> testRatings) {int hits = 0;int total = 0;for (User user : users) {// 获取用户实际高评分视频(4分以上)Set<Integer> actualHighRated = testRatings.stream().filter(r -> r.getUserId() == user.getId() && r.getScore() >= 4).map(Rating::getVideoId).collect(Collectors.toSet());if (!actualHighRated.isEmpty()) {// 获取推荐视频List<Integer> recommended = model.recommendVideos(user.getId(), 5, videos.size());// 计算命中数for (int videoId : recommended) {if (actualHighRated.contains(videoId)) {hits++;}}total += recommended.size();}}return total > 0 ? (double) hits / total : 0;
}private double calculateRecall(ALS model, List<Rating> testRatings) {int hits = 0;int totalHighRated = 0;for (User user : users) {// 获取用户实际高评分视频(4分以上)Set<Integer> actualHighRated = testRatings.stream().filter(r -> r.getUserId() == user.getId() && r.getScore() >= 4).map(Rating::getVideoId).collect(Collectors.toSet());totalHighRated += actualHighRated.size();if (!actualHighRated.isEmpty()) {// 获取推荐视频List<Integer> recommended = model.recommendVideos(user.getId(), 5, videos.size());// 计算命中数for (int videoId : recommended) {if (actualHighRated.contains(videoId)) {hits++;}}}}return totalHighRated > 0 ? (double) hits / totalHighRated : 0;
}
这个实现提供了基于ALS的教育视频推荐系统完整框架，可以根据实际需求进一步扩展和优化。系统包含核心推荐算法、业务逻辑和评估模块，适合作为学术研究或中小型教育平台的推荐系统基础。